Gastos Casa
26 septiembre, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 8/8/2022 | Comida | 17890 | Tami | NA |
| 8/8/2022 | Comida | 41390 | Tami | NA |
| 19/8/2022 | VTR | 21990 | Andrés | NA |
| 18/8/2022 | Comida | 21860 | Andrés | NA |
| 19/8/2022 | Comida | 5213 | Andrés | NA |
| 22/8/2022 | Parafina | 23300 | Tami | NA |
| 24/8/2022 | Comida | 57780 | Tami | NA |
| 26/8/2022 | mantencion toyotomi | 34000 | Andrés | mantencion toyotomi |
| 27/8/2022 | Comida | 19410 | Tami | NA |
| 29/8/2022 | Netflix | 8320 | Tami | NA |
| 31/8/2022 | Incoludido | 21000 | Tami | NA |
| 31/8/2022 | Electricidad | 89272 | Andrés | PAC ENEL 01686518 |
| 31/8/2022 | Enceres | 12000 | Andrés | Visita gasfiter |
| 3/9/2022 | Comida | 59225 | Andrés | Lider |
| 3/9/2022 | Comida | 21350 | Andrés | Laflordeloto.cl |
| 5/9/2022 | Comida | 78035 | Tami | NA |
| 7/9/2022 | Enceres | 15400 | Andrés | era del mes pasado pero igual |
| 10/9/2022 | Agua | 10860 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 10/9/2022 | basureros | 20000 | Andrés | basureros |
| 10/9/2022 | Comida | 41870 | Andrés | NA |
| 12/9/2022 | Comida | 3020 | Andrés | Reste el costa rama |
| 12/9/2022 | Comida | 20563 | Andrés | Laflordeloto.cl |
| 12/9/2022 | Enceres | 57000 | Andrés | Arreglo wc+ visita |
| 15/9/2022 | Comida | 38863 | Tami | NA |
| 16/9/2022 | Diosi | 12136 | Tami | Pipeta antipulgas |
| 19/9/2022 | Comida | 5070 | Andrés | NA |
| 20/9/2022 | Comida | 62208 | Tami | NA |
| 21/9/2022 | VTR | 17990 | Andrés | Entel con mercadopago |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.3197e+08 2 4.7807 0.0088 **
## lag_depvar 7.6502e+10 1 1693.3311 <2e-16 ***
## Residuals 2.2228e+10 492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 907.0548 13550.62 0.0202764
## 2-0 27581.384 21750.6611 33412.11 0.0000000
## 2-1 20352.546 16820.1610 23884.93 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
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## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
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## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 36 30784.86 0 33634.57
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 340 49815.65 16046.644
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2059.953113 4057.969548 -555.505010 2422.958594 -3004.104781
## 7 8 9 10 11
## 506.216054 -5674.271155 -1166.897237 -3943.045234 -372.919776
## 12 13 14 15 16
## -4901.082722 -1543.740429 -834.489515 437.275838 -3197.054057
## 17 18 19 20 21
## -318.179005 -2078.900511 6660.532553 -1531.473814 -1203.025014
## 22 23 24 25 26
## 1485.167788 -1192.687399 234.130269 1689.104901 -7123.175724
## 27 28 29 30 31
## 976.721515 8207.417845 368.602224 -63.977757 -2447.789783
## 32 33 34 35 36
## 1548.643495 4533.415054 1056.140910 2317.857933 -1953.167425
## 37 38 39 40 41
## 4543.378713 4265.686698 -2340.023871 -3021.491947 -1123.913442
## 42 43 44 45 46
## -10745.766335 7362.821539 2568.491952 1357.885636 8087.159441
## 47 48 49 50 51
## 611.604286 6458.373135 6605.467278 -6026.276760 -4879.026929
## 52 53 54 55 56
## -5098.425316 -7926.129179 6188.904758 -4069.548686 -4859.027865
## 57 58 59 60 61
## 3921.677797 917.217755 -13.176564 158.684140 -4983.397356
## 62 63 64 65 66
## 18173.939624 3550.658809 -3751.322201 5859.508846 7243.486845
## 67 68 69 70 71
## 14497.798493 1464.059554 -13425.326088 -1396.603106 4573.726049
## 72 73 74 75 76
## -4995.017534 -4452.083603 -10507.313769 2534.143478 -5358.896042
## 77 78 79 80 81
## 1138.470227 -6808.839212 646.717285 -2271.493241 -2604.358098
## 82 83 84 85 86
## -3834.285304 -423.876408 2417.638135 3835.660753 513.046261
## 87 88 89 90 91
## -456.774041 224.052188 4324.151180 -1175.157997 1148.359012
## 92 93 94 95 96
## -2075.313038 -1039.087148 189.412500 283.527959 -7478.695617
## 97 98 99 100 101
## 2450.573405 -8568.703765 -2848.608710 -3937.751958 -1618.468130
## 102 103 104 105 106
## -1145.376540 3291.407333 -2268.875168 2674.791393 -1106.957319
## 107 108 109 110 111
## 1023.594244 2626.192554 -3139.336214 -4687.203190 -784.781073
## 112 113 114 115 116
## 1966.712086 11734.663086 -1292.067555 2634.067857 4213.060599
## 117 118 119 120 121
## 3427.935569 -1190.949800 -4788.052489 -3752.638003 2321.744836
## 122 123 124 125 126
## -1748.006298 1339.415513 8847.403511 772.956468 59.285014
## 127 128 129 130 131
## -2584.609793 2617.852056 7000.468793 915.423198 -8591.769288
## 132 133 134 135 136
## 1730.093634 4105.841394 -3220.223597 -1446.126543 -866.951944
## 137 138 139 140 141
## -3885.367238 1206.371367 -483.940483 -2900.158012 1750.879069
## 142 143 144 145 146
## -1865.251156 -7802.033554 2120.027721 -3424.428616 2175.577436
## 147 148 149 150 151
## -209.007032 1066.929876 -328.714198 1381.214576 1201.822947
## 152 153 154 155 156
## 3360.891906 -4882.772154 -1157.698862 -3212.888853 6000.024808
## 157 158 159 160 161
## 9740.810373 -3090.512457 -4422.815793 3981.762152 530.738559
## 162 163 164 165 166
## 3020.342545 -5615.225377 -6414.158259 4529.442610 17719.117451
## 167 168 169 170 171
## 3809.029205 -237.550631 -2271.723550 -902.552011 3805.682035
## 172 173 174 175 176
## -37.276673 -7876.410825 3129.895786 4566.398732 833.020442
## 177 178 179 180 181
## 8957.089903 -9105.328093 -3245.238983 -10489.814859 -10904.590923
## 182 183 184 185 186
## 1644.813744 9672.102872 -1140.953644 6221.994395 6794.478743
## 187 188 189 190 191
## 13343.677585 8515.408029 -4030.729342 2550.222960 10448.697668
## 192 193 194 195 196
## -1631.687837 -2396.104328 -10193.290549 -6178.511368 1473.105460
## 197 198 199 200 201
## -5006.292887 -9527.693852 5725.399756 -2783.723322 -1410.310725
## 202 203 204 205 206
## -497.593758 6797.236648 10120.524781 730.886478 3078.893801
## 207 208 209 210 211
## 3234.688974 5904.518193 12917.319777 -5693.587834 -11228.627731
## 212 213 214 215 216
## -5487.576444 -10356.756338 -4758.267726 1874.448854 -12690.121852
## 217 218 219 220 221
## 16805.484346 8066.347546 1715.057542 26865.546208 12484.138706
## 222 223 224 225 226
## 7217.850071 13887.403264 -4127.913268 -1871.179823 3703.407694
## 227 228 229 230 231
## 290.211204 2709.177515 8977.461758 5761.446172 -1984.409257
## 232 233 234 235 236
## -1851.900173 9448.160250 -11538.647765 -7187.687589 -8370.030141
## 237 238 239 240 241
## -9853.917781 3405.180249 1641.531237 -8026.849312 -8656.394834
## 242 243 244 245 246
## 9488.930456 -7467.621993 2840.217782 -9985.022478 -3663.840936
## 247 248 249 250 251
## 1825.704770 1371.577534 -11974.297127 4072.478140 2434.957869
## 252 253 254 255 256
## 4547.438207 2419.735674 -902.796796 11400.700241 21040.662059
## 257 258 259 260 261
## 3181.379558 -4289.511375 4153.596525 -1667.113076 3800.001930
## 262 263 264 265 266
## -4804.280609 -10785.892078 -4515.916788 -269.419348 -4937.341567
## 267 268 269 270 271
## 9067.674712 -4078.406748 4427.397780 -1910.972422 4644.640287
## 272 273 274 275 276
## 881.179816 7470.904615 -1306.382532 12152.570432 -4557.952831
## 277 278 279 280 281
## 1809.897308 -291.797112 7947.321926 -5021.768032 -2631.797234
## 282 283 284 285 286
## -11125.838558 -2423.635750 18919.544244 7869.624173 2760.712035
## 287 288 289 290 291
## -608.406524 951.483337 6451.962249 6892.749723 -18804.813292
## 292 293 294 295 296
## -10968.436609 -7841.114663 10014.474385 3315.365140 -968.615734
## 297 298 299 300 301
## 27623.195970 10024.719072 4792.486825 9399.088468 2684.832854
## 302 303 304 305 306
## -1187.250272 7793.796436 -24436.840263 -3399.831179 4.628188
## 307 308 309 310 311
## -6780.963767 -3712.158281 3227.252254 -8932.799254 -2883.803330
## 312 313 314 315 316
## -7820.477718 1997.046683 -2757.194970 2454.504697 -3717.206501
## 317 318 319 320 321
## 27834.129894 -645.310769 3390.665924 10908.826450 5574.748890
## 322 323 324 325 326
## 32336.592044 4778.540105 -21253.164823 1748.468032 1080.534109
## 327 328 329 330 331
## -6475.797054 -1652.548787 -33151.155925 1380.356218 -1835.799622
## 332 333 334 335 336
## 376.604568 -2717.868813 4548.684747 -36.680911 -6561.830258
## 337 338 339 340 341
## -2667.778363 -1730.433942 -7216.762255 4371.864085 -919.348912
## 342 343 344 345 346
## -1292.610584 -550.713547 610.367423 894.772573 -1227.198376
## 347 348 349 350 351
## -9053.290789 -12734.597964 2897.107296 -3797.461025 -3117.941730
## 352 353 354 355 356
## -5433.033981 2327.203942 1905.717373 3228.793126 -3346.426818
## 357 358 359 360 361
## -77.103757 1099.746711 7408.572354 579.869920 255.393689
## 362 363 364 365 366
## 2871.703910 -2493.552626 -591.138507 -8450.324820 -4241.118178
## 367 368 369 370 371
## -5790.154735 -4477.019608 -6749.411191 5569.642976 836.902039
## 372 373 374 375 376
## 7557.229464 -7297.297404 -1849.807930 -2966.637488 -2026.185821
## 377 378 379 380 381
## -12009.218554 2465.578295 -10126.328270 6291.066379 9829.697476
## 382 383 384 385 386
## 3489.076250 -2083.694139 1937.827292 7049.531848 11634.296235
## 387 388 389 390 391
## -5704.423751 -5189.729649 79.213378 8800.709405 1956.597719
## 392 393 394 395 396
## 11352.275184 -9862.633168 2919.431868 835.451138 688.281697
## 397 398 399 400 401
## -523.267590 -414.822049 -14323.985280 8860.278260 -943.784448
## 402 403 404 405 406
## -1119.003518 7251.694049 -7742.017422 -1008.666276 -2229.441643
## 407 408 409 410 411
## -5490.237640 -2468.712047 -3505.749479 -8313.751388 6655.689314
## 412 413 414 415 416
## 2064.875201 -6985.749371 -7234.887630 14744.024657 4145.284570
## 417 418 419 420 421
## 4765.408108 -7818.967281 -4432.033822 -2239.274131 3201.908671
## 422 423 424 425 426
## -13673.133554 -2302.126514 -8601.419678 3588.571438 7483.687811
## 427 428 429 430 431
## 6974.081733 -3680.512086 -3773.705737 -4337.046412 -1363.637907
## 432 433 434 435 436
## -5283.002273 -6151.068221 -5422.485178 -828.316367 -300.897106
## 437 438 439 440 441
## -4450.559202 3132.798022 5328.542152 -4648.521246 -1709.355811
## 442 443 444 445 446
## 2028.905490 -3423.526884 3275.834320 -6189.478582 -11661.402748
## 447 448 449 450 451
## -3946.946605 10226.933332 -1592.836417 5197.809464 -5498.001638
## 452 453 454 455 456
## -696.082181 806.376153 3428.414674 -11913.830772 3850.974029
## 457 458 459 460 461
## -6279.718118 7001.647885 3396.786974 2846.278406 -3540.967478
## 462 463 464 465 466
## 2437.517672 307.184038 2103.905843 -233.400938 3643.347059
## 467 468 469 470 471
## -2384.470580 6092.952962 -6717.966415 -2654.537999 -1863.350263
## 472 473 474 475 476
## -4301.684745 3403.193561 8158.541126 -5749.423154 1821.736904
## 477 478 479 480 481
## -5861.961338 -2461.392378 2416.786862 -12559.259237 -9252.518837
## 482 483 484 485 486
## -618.315436 583.312024 -430.999575 -828.159378 -9082.384290
## 487 488 489 490 491
## 11676.463819 6662.292115 7763.705479 -5180.011831 5687.167245
## 492 493 494 495 496
## 9551.266621 6214.777073 -13363.349549 -10287.086275 -3044.358630
## 497
## -682.232315
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17209.33 20081.03 24371.65 24087.18 26460.82 23770.50 24492.99 19684.04
## 10 11 12 13 14 15 16 17
## 19418.33 16738.21 17522.37 14223.60 14275.20 14945.58 16656.77 14962.32
## 18 19 20 21 22 23 24 25
## 16005.90 15374.04 22517.47 21593.60 21068.98 22975.26 22295.44 22953.61
## 26 27 28 29 30 31 32 33
## 24815.46 18691.56 20432.58 28337.40 28395.55 28065.65 25674.64 27089.16
## 34 35 36 37 38 39 40 41
## 30965.29 31316.71 32738.02 30227.19 34177.31 37413.02 34443.78 31227.20
## 42 43 44 45 46 47 48 49
## 30065.05 20563.46 28146.94 30604.40 31702.98 38599.97 38090.20 42792.53
## 50 51 52 53 54 55 56 57
## 47065.28 39700.31 34222.00 29201.84 22287.24 28631.41 25182.60 21448.32
## 58 59 60 61 62 63 64 65
## 25894.64 27165.03 27464.60 27879.97 23715.35 40449.48 42309.32 37514.35
## 66 67 68 69 70 71 72 73
## 41757.51 46715.49 57475.51 55472.18 40588.32 38072.70 41116.59 35367.66
## 74 75 76 77 78 79 80 81
## 30780.74 21404.14 24633.18 20523.82 22627.84 17479.43 19512.21 18732.07
## 82 83 84 85 86 87 88 89
## 17751.43 15803.73 17092.50 20731.62 25187.38 26185.77 26210.95 26832.99
## 90 91 92 93 94 95 96 97
## 30993.59 29814.07 30822.03 28869.80 28062.73 28434.04 28844.12 22366.28
## 98 99 100 101 102 103 104 105
## 25407.28 18377.75 17224.04 15247.90 15550.23 16233.45 20744.59 19820.21
## 106 107 108 109 110 111 112 113
## 23361.53 23149.69 24840.24 27741.76 25218.35 21631.21 21909.00 24578.05
## 114 115 116 117 118 119 120 121
## 35536.07 33713.36 35566.65 38590.78 40563.52 38232.05 33008.50 29318.40
## 122 123 124 125 126 127 128 129
## 31419.15 29684.30 30876.03 38541.19 38180.57 37234.04 34070.58 35867.10
## 130 131 132 133 134 135 136 137
## 41311.43 40746.91 31872.91 33148.59 36365.80 32745.56 31118.95 30196.08
## 138 139 140 141 142 143 144 145
## 26723.49 28150.08 27917.73 25584.12 27625.97 26238.89 19785.97 22842.57
## 146 147 148 149 150 151 152 153
## 20650.57 23653.29 24197.93 25802.00 25985.64 27654.03 28965.97 32024.20
## 154 155 156 157 158 159 160 161
## 27455.41 26712.03 24246.26 30191.05 41110.94 39426.82 36769.09 41832.55
## 162 163 164 165 166 167 168 169
## 43253.23 46698.51 42125.44 37392.27 42864.17 59306.54 61537.69 59938.15
## 170 171 172 173 174 175 176 177
## 56736.55 55122.03 57847.85 56863.55 49089.39 51937.17 55711.98 55748.48
## 178 179 180 181 182 183 184 185
## 62938.61 53359.24 50082.24 40811.88 32278.47 35816.90 46007.24 45458.58
## 186 187 188 189 190 191 192 193
## 51462.52 57256.89 68132.59 73460.87 67101.35 67296.45 74427.54 70066.82
## 194 195 196 197 198 199 200 201
## 65551.15 54702.51 48681.32 50117.86 45674.69 37776.17 44256.15 42468.31
## 202 203 204 205 206 207 208 209
## 42103.17 42585.62 49438.05 58403.68 58030.11 59769.74 61439.77 65263.54
## 210 211 212 213 214 215 216 217
## 74811.44 66826.20 54913.72 49476.18 40395.12 37326.69 40467.12 30401.52
## 218 219 220 221 222 223 224 225
## 47520.94 54904.66 55814.31 78775.43 86334.86 88355.31 96011.91 86885.04
## 226 227 228 229 230 231 232 233
## 80831.88 80410.22 77031.39 76185.68 80963.41 82339.41 76727.04 71898.84
## 234 235 236 237 238 239 240 241
## 77601.08 64134.12 56102.17 47983.63 39523.11 43751.04 45922.28 39316.68
## 242 243 244 245 246 247 248 249
## 32941.93 43312.76 37510.21 41479.74 33677.13 32371.87 36058.57 38906.73
## 250 251 252 253 254 255 256 257
## 29657.38 35646.47 39480.56 44719.98 47461.65 46949.87 57339.34 74986.91
## 258 259 260 261 262 263 264 265
## 74800.37 68053.55 69548.11 65736.43 67194.99 60899.03 50081.49 46074.71
## 266 267 268 269 270 271 272 273
## 46285.91 42359.18 51238.98 47480.03 51662.40 49762.79 53865.11 54163.67
## 274 275 276 277 278 279 280 281
## 60232.81 57846.72 67602.81 61475.39 61687.23 60022.11 65814.34 59490.94
## 282 283 284 285 286 287 288 289
## 56025.27 45487.78 43870.74 61251.09 66828.72 67241.69 64637.09 63716.61
## 290 291 292 293 294 295 296 297
## 67751.96 71695.81 52529.01 42545.97 36505.53 46915.63 50185.33 49291.66
## 298 299 300 301 302 303 304 305
## 73696.00 79692.51 80365.91 85018.02 83201.11 78188.63 81685.27 56368.26
## 306 307 308 309 310 311 312 313
## 52597.23 52274.25 46011.02 43196.46 46830.80 39318.95 38030.05 32544.81
## 314 315 316 317 318 319 320 321
## 36361.91 35536.21 39400.64 37367.73 63375.88 61198.48 62836.03 70902.97
## 322 323 324 325 326 327 328 329
## 73310.84 99011.75 97375.45 72997.67 71785.18 70128.37 62010.83 59108.30
## 330 331 332 333 334 335 336 337
## 28798.07 32517.37 32960.68 35300.58 34635.74 40452.40 41537.26 36743.92
## 338 339 340 341 342 343 344 345
## 35951.58 36079.33 31357.99 37408.63 38077.75 38338.43 39221.78 41023.08
## 346 347 348 349 350 351 352 353
## 42860.77 42610.29 35494.17 25980.75 31371.46 30222.66 29809.18 27405.08
## 354 355 356 357 358 359 360 361
## 32124.28 35910.92 40413.00 38586.39 39857.54 42014.43 49473.42 50028.75
## 362 363 364 365 366 367 368 369
## 50232.15 52716.55 50178.28 49618.04 42199.83 39372.44 35516.45 33275.98
## 370 371 372 373 374 375 376 377
## 29299.79 36650.53 38957.20 46910.73 40830.38 40272.78 38797.47 38326.22
## 378 379 380 381 382 383 384 385
## 29115.14 33752.90 26744.65 35034.87 45457.07 49053.27 47311.74 49320.61
## 386 387 388 389 390 391 392 393
## 55594.42 65161.71 58314.44 52734.93 52461.29 59904.55 60432.44 69175.92
## 394 395 396 397 398 399 400 401
## 58187.57 59767.98 59324.29 58803.70 57277.54 56028.41 42672.72 51332.50
## 402 403 404 405 406 407 408 409
## 50324.29 49281.59 55738.16 48216.24 47521.44 45833.67 41473.57 40294.18
## 410 411 412 413 414 415 416 417
## 38341.32 32384.45 40325.27 43276.89 37903.17 32948.98 47949.14 51827.16
## 418 419 420 421 422 423 424 425
## 55790.40 48194.46 44485.99 43150.52 46767.99 35086.98 34813.85 29023.00
## 426 427 428 429 430 431 432 433
## 34661.17 43060.78 50012.51 46749.99 43793.33 40691.92 40579.15 37026.50
## 434 435 436 437 438 439 440 441
## 33131.49 30341.60 31931.33 33796.70 31784.06 36692.31 42951.52 39675.78
## 442 443 444 445 446 447 448 449
## 39379.24 42411.67 40279.45 44303.48 39509.26 30463.95 29291.35 40746.55
## 450 451 452 453 454 455 456 457
## 40425.33 46125.43 41723.80 42076.48 43711.01 47461.40 37248.03 42139.29
## 458 459 460 461 462 463 464 465
## 37522.92 45157.50 48708.01 51351.25 48052.48 50413.53 50616.81 52378.97
## 466 467 468 469 470 471 472 473
## 51872.22 54841.47 52146.62 57241.54 50443.11 48033.35 46607.26 43202.38
## 474 475 476 477 478 479 480 481
## 46991.03 54518.99 48897.69 50615.68 45359.39 43724.36 46581.83 35904.38
## 482 483 484 485 486 487 488 489
## 29410.17 31295.69 34015.71 35518.59 36492.81 30078.54 42717.28 49435.15
## 490 491 492 493 494 495 496 497
## 56324.58 50990.26 55865.16 63564.94 67409.35 53546.66 44042.93 42050.80
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8529
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.78069 0.5572868 2.89241
## t2* 1693.33106 27.1758158 241.48170
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.421938 4.960141 10.65103
## 2 lag_depvar 1350.264574 1704.691123 2145.96890Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 26 01:03:49 2022
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# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 5.496500 | 5.70810 | 7.294219 |
| Comida | NA | 285.084375 | 304.77170 | 337.832438 |
| Comunicaciones | NA | 0.000000 | 0.00000 | 0.000000 |
| Electricidad | NA | 48.842750 | 37.25090 | 31.008531 |
| Enceres | NA | 13.849375 | 14.42045 | 23.642281 |
| Farmacia | NA | 2.747500 | 9.49665 | 11.199188 |
| Gas/Bencina | NA | 56.943750 | 30.92770 | 25.801687 |
| Diosi | NA | 16.628375 | 38.26405 | 37.835531 |
| donaciones/regalos | NA | 0.000000 | 8.60410 | 8.584969 |
| Electrodomésticos/ Mantención casa | NA | 5.916000 | 36.32340 | 25.920875 |
| VTR | NA | 27.240000 | 22.34815 | 21.135250 |
| Netflix | NA | 7.607125 | 7.26010 | 7.630281 |
| Otros | NA | 4.726625 | 1.89065 | 1.181656 |
| Total | 0 | 475.082375 | 517.26595 | 539.066906 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1743, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-10-09 00:04:58 sería de: 36.024 pesos// Percentil 95% más alto proyectado: 39.527,33
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34620.70 | 34573.97 |
| Lo.80 | 34807.56 | 34841.43 |
| Point.Forecast | 36024.04 | 38454.70 |
| Hi.80 | 37925.43 | 43096.21 |
| Hi.95 | 38971.37 | 45553.27 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3573 986.4061
## s.e. 0.1481 38.4438
##
## sigma^2 = 27945: log likelihood = -280.18
## AIC=566.35 AICc=566.97 BIC=571.63paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.4100 33.1028
## s.e. 0.1482 1.4427
##
## sigma^2 = 29234: log likelihood = -281.17
## AIC=568.34 AICc=568.95 BIC=573.62forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 922.8188 | 635.6049 | 664.3815 |
| Lo.80 | 1049.9955 | 757.0292 | 743.5117 |
| Point.Forecast | 1290.2380 | 986.4050 | 919.6053 |
| Hi.80 | 1530.4805 | 1215.7809 | 1216.7059 |
| Hi.95 | 1657.6572 | 1337.2051 | 1411.0561 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.1
## [7] ggiraph_0.8.3 tidytext_0.3.4 DT_0.25
## [10] autoplotly_0.1.4 rvest_1.0.3 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.17.0 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.2 forcats_0.5.2
## [22] dplyr_1.0.10 purrr_0.3.4 tidyr_1.2.1
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.8 httr_1.4.4
## [34] readxl_1.4.1 zoo_1.8-11 stringr_1.4.1
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0.5 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.40 uuid_1.1-0 rstudioapi_0.14
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.1-1
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.6.1 fBasics_4021.92 rprojroot_2.0.3
## [25] vctrs_0.4.1 generics_0.1.3 xfun_0.33
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.5.7
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.1
## [37] spatial_7.3-14 timeDate_4021.104 rlang_1.0.6
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.1 selectr_0.4-2
## [46] broom_1.0.1 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.9 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.3 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
## [58] Rcpp_1.0.9 base64enc_0.1-3 fracdiff_1.5-1
## [61] haven_2.5.1 fs_1.5.2 magrittr_2.0.3
## [64] timeSeries_4021.104 lmtest_0.9-40 reprex_2.0.2
## [67] googledrive_2.0.0 mvtnorm_1.1-3 sjmisc_2.8.9
## [70] hms_1.1.2 evaluate_0.16 xtable_1.8-4
## [73] sjstats_0.18.1 ggeffects_1.1.3 compiler_4.1.2
## [76] KernSmooth_2.23-20 crayon_1.5.1 minqa_1.2.4
## [79] htmltools_0.5.3 tzdb_0.3.0 lubridate_1.8.0
## [82] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [85] boot_1.3-28 Matrix_1.5-1 car_3.1-0
## [88] cli_3.4.1 quadprog_1.5-8 parallel_4.1.2
## [91] insight_0.18.4 igraph_1.3.5 pkgconfig_2.0.3
## [94] xml2_1.3.3 bslib_0.4.0 estimability_1.4.1
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_1.0.0
## [100] digest_0.6.29 parameters_0.18.2 janitor_2.1.0
## [103] rmarkdown_2.16 cellranger_1.1.0 curl_4.3.2
## [106] gtools_3.9.3 urca_1.3-3 nloptr_2.0.3
## [109] lifecycle_1.0.2 nlme_3.1-153 jsonlite_1.8.0
## [112] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.1
## [115] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.13.0 bit_4.0.4
## [121] sass_0.4.2 performance_0.9.2 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))